{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#HW2: Logistic 回归&SVM\n",
    "#Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集）进行分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入数据包\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据读取\n",
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据统计\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'times of occurred')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089c2af080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#diabetes发生和未发生次数直方图\n",
    "sns.countplot(data.Outcome)\n",
    "plt.xlabel('diabetes or not')\n",
    "plt.ylabel('times of occurred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Times of occurred')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089c7daa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#单个特征与diabetes之间的关系\n",
    "#观察Pregnancies为离散型特征\n",
    "#定义figure\n",
    "fig = plt.figure()\n",
    "sns.countplot(data.Pregnancies)\n",
    "plt.xlabel('Number of Pregnancies')\n",
    "plt.ylabel('Times of occurred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Times of occurred')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089c3d2cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察怀孕的数据发现大于14次以上的都是得病的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Times of occurred')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089c420e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察Glucose为连续型特征（用直方图）\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.Glucose.values,bins=30,kde=False)\n",
    "plt.xlabel('Number of Glucoses')\n",
    "plt.ylabel('Times of occurred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Times of occurred')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089c682518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察BloodPressure为连续型特征（用直方图）\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.BloodPressure.values,bins=30,kde=False)\n",
    "plt.xlabel('Number of BloodPressure')\n",
    "plt.ylabel('Times of occurred')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Times of occurred')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089c7b4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察SkinThickness为连续型特征（用直方图）\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.SkinThickness.values,bins=15,kde=False)\n",
    "plt.xlabel('Number of SkinThickness')\n",
    "plt.ylabel('Times of occurred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Times of occurred')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089dbb9a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察Insulin为连续型特征\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.Insulin.values,bins=20,kde=False)\n",
    "plt.xlabel('Number of Insulin')\n",
    "plt.ylabel('Times of occurred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Times of occurred')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089c49eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察BMI为连续型特征\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.BMI.values,bins=30,kde=False)\n",
    "plt.xlabel('Number of BMI')\n",
    "plt.ylabel('Times of occurred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Times of occurred')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089dc72940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察DiabetesPedigreeFunction为连续型特征\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.DiabetesPedigreeFunction.values,bins=30,kde=False)\n",
    "plt.xlabel('Number of DiabetesPedigreeFunction')\n",
    "plt.ylabel('Times of occurred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Times of occurred')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089dd90eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察Age为连续型特征\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.Age.values,bins=30,kde=False)\n",
    "plt.xlabel('Number of Age')\n",
    "plt.ylabel('Times of occurred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2089e0d2630>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089e169128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#特征之间的相关性\n",
    "data_corr = data.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(10, 10))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                   3.0000\n",
      "Glucose                     117.0000\n",
      "BloodPressure                72.0000\n",
      "SkinThickness                23.0000\n",
      "Insulin                      30.5000\n",
      "BMI                          32.0000\n",
      "DiabetesPedigreeFunction      0.3725\n",
      "Age                          29.0000\n",
      "Outcome                       0.0000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#数据预处理\n",
    "#将特征中数据为0的值用其平均值代替\n",
    "#计算平均值\n",
    "median = data.median()\n",
    "print(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将平均值替代数值中为0的特征项\n",
    "data['Pregnancies']=data['Pregnancies'].replace(0,3)\n",
    "data['Glucose']=data['Glucose'].replace(0,117)\n",
    "data['BloodPressure']=data['BloodPressure'].replace(0,72)\n",
    "data['SkinThickness']=data['SkinThickness'].replace(0,23)\n",
    "data['Insulin']=data['Insulin'].replace(0,30.5)\n",
    "data['BMI']=data['BMI'].replace(0,32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.278646</td>\n",
       "      <td>121.656250</td>\n",
       "      <td>72.386719</td>\n",
       "      <td>27.334635</td>\n",
       "      <td>94.652344</td>\n",
       "      <td>32.450911</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.021516</td>\n",
       "      <td>30.438286</td>\n",
       "      <td>12.096642</td>\n",
       "      <td>9.229014</td>\n",
       "      <td>105.547598</td>\n",
       "      <td>6.875366</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      4.278646  121.656250      72.386719      27.334635   94.652344   \n",
       "std       3.021516   30.438286      12.096642       9.229014  105.547598   \n",
       "min       1.000000   44.000000      24.000000       7.000000   14.000000   \n",
       "25%       2.000000   99.750000      64.000000      23.000000   30.500000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   31.250000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    32.450911                  0.471876   33.240885    0.348958  \n",
       "std      6.875366                  0.331329   11.760232    0.476951  \n",
       "min     18.200000                  0.078000   21.000000    0.000000  \n",
       "25%     27.500000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#最小值没有为0的数值，替代成功"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 614 entries, 720 to 688\n",
      "Data columns (total 8 columns):\n",
      "Pregnancies                 614 non-null int64\n",
      "Glucose                     614 non-null int64\n",
      "BloodPressure               614 non-null int64\n",
      "SkinThickness               614 non-null int64\n",
      "Insulin                     614 non-null float64\n",
      "BMI                         614 non-null float64\n",
      "DiabetesPedigreeFunction    614 non-null float64\n",
      "Age                         614 non-null int64\n",
      "dtypes: float64(3), int64(5)\n",
      "memory usage: 43.2 KB\n"
     ]
    }
   ],
   "source": [
    "#数据分割\n",
    "#随机选择20%的数据作为测试集 剩余的数据为训练集\n",
    "x=data.drop('Outcome',axis=1)\n",
    "y=data['Outcome'].values\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(x,y,random_state=50,test_size=0.2)\n",
    "X_train.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ss_X=StandardScaler()\n",
    "\n",
    "X_train=ss_X.fit_transform(X_train)\n",
    "X_test=ss_X.fit_transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is: [0.41726153 0.48787416 0.44034917 0.4670968  0.49993211]\n",
      "cv logloss is: 0.46250275567297416\n"
     ]
    }
   ],
   "source": [
    "#logistic回归 缺省情况下\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print (\"logloss of each fold is:\" ,-loss)\n",
    "print(\"cv logloss is:\", -loss.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#选择最佳的正则函数及正则参数\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4624417958091314\n",
      "{'C': 1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# examine the best model\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089eae1cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    pyplot.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    pyplot.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'neg-logloss' )\n",
    "pyplot.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#观察纵轴数越大性能越好，当C=1时性能最好（L2正则）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "           class_weight=None, cv=5, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1.0, max_iter=100, multi_class='ovr',\n",
       "           n_jobs=1, penalty='l1', random_state=None, refit=True,\n",
       "           scoring='neg_log_loss', solver='liblinear', tol=0.0001,\n",
       "           verbose=0)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#L1正则参数\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "\n",
    "# LogisticRegressionCV比GridSearchCV快\n",
    "lrcv_L1 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l1', solver='liblinear', multi_class='ovr')\n",
    "lrcv_L1.fit(X_train, y_train)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.69314718, -0.6617603 , -0.44055857, -0.41830308, -0.41645172,\n",
       "         -0.41627705, -0.41625869],\n",
       "        [-0.69314718, -0.66048819, -0.504724  , -0.48942643, -0.48832201,\n",
       "         -0.48823818, -0.48822551],\n",
       "        [-0.69314718, -0.65657026, -0.46805738, -0.4425699 , -0.43966582,\n",
       "         -0.4393975 , -0.43936783],\n",
       "        [-0.69314718, -0.65975568, -0.45523012, -0.46473693, -0.46973441,\n",
       "         -0.47029602, -0.47035328],\n",
       "        [-0.69314718, -0.65635776, -0.48742894, -0.49878588, -0.50218274,\n",
       "         -0.50250143, -0.50253691]])}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L1.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "           class_weight=None, cv=5, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1.0, max_iter=100, multi_class='ovr',\n",
       "           n_jobs=1, penalty='l2', random_state=None, refit=True,\n",
       "           scoring='neg_log_loss', solver='liblinear', tol=0.0001,\n",
       "           verbose=0)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#L2正则参数\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "\n",
    "\n",
    "lrcv_L2 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l2', solver='liblinear', multi_class='ovr')\n",
    "lrcv_L2.fit(X_train, y_train)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.63270793, -0.49795414, -0.42734882, -0.41726153, -0.41635282,\n",
       "         -0.41626466, -0.41625588],\n",
       "        [-0.6393786 , -0.53238989, -0.48943256, -0.48787416, -0.48817963,\n",
       "         -0.48821918, -0.48822323],\n",
       "        [-0.63833748, -0.5184363 , -0.45068483, -0.44034917, -0.43945643,\n",
       "         -0.43937106, -0.43936257],\n",
       "        [-0.63591865, -0.51196767, -0.45871487, -0.4670968 , -0.46999272,\n",
       "         -0.47032488, -0.47035865],\n",
       "        [-0.63872303, -0.52641371, -0.4908907 , -0.49993211, -0.50225835,\n",
       "         -0.50251857, -0.50254489]])}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrcv_L2.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7768729641693811\n",
      "{'C': 0.1}\n"
     ]
    }
   ],
   "source": [
    "#线性SVM，比较Logistic回归的性能\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "param_grid = {'C': Cs}\n",
    "grid = GridSearchCV(SVC(kernel='linear'), param_grid, cv=5)\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SVM线性正则参数调优\n",
    "def fit_grid_point_Linear(C, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC1 =  LinearSVC( C = C)\n",
    "    SVC1 = SVC1.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC1.score(X_test, y_test)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7207792207792207\n",
      "accuracy: 0.7207792207792207\n",
      "accuracy: 0.7077922077922078\n",
      "accuracy: 0.7012987012987013\n",
      "accuracy: 0.7337662337662337\n",
      "accuracy: 0.7077922077922078\n",
      "accuracy: 0.6298701298701299\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x208a02aaa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "from sklearn.svm import LinearSVC\n",
    "C_s = np.logspace(-3, 3, 7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "#penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train, y_train, X_test, y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "#for j, penalty in enumerate(penalty_s):\n",
    "pyplot.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "    \n",
    "\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "pyplot.savefig('SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#正确率最好的性能为0.7337比较logistic回归的性能0.46有了提升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RBF核的SVM\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC3 =  SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC3.score(X_test, y_test)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6558441558441559\n",
      "accuracy: 0.6558441558441559\n",
      "accuracy: 0.6558441558441559\n",
      "accuracy: 0.6623376623376623\n",
      "accuracy: 0.6558441558441559\n",
      "accuracy: 0.6558441558441559\n",
      "accuracy: 0.6883116883116883\n",
      "accuracy: 0.7402597402597403\n",
      "accuracy: 0.6558441558441559\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.7207792207792207\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.7207792207792207\n",
      "accuracy: 0.7142857142857143\n",
      "accuracy: 0.7272727272727273\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-1, 2, 4)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-5, -2, 4)  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "        accuracy_s.append(tmp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2089f073828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    pyplot.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'log(C)' )                                                                                                      \n",
    "pyplot.ylabel( 'accuracy' )\n",
    "pyplot.savefig('RBF_SVM_Otto.png' )\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "C越大 蓝色gamma值最小"
   ]
  }
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